Materials Science at IgMin Research | Engineering Group

The NASA approach for technology development for missions is to (1) wait for a mission need, and (2) upgrade the technology available at that time, however inadequate. This is illustrated with two important NASA technologies: in situ resource utilization (ISRU) and recycling wastewater. It also serves as a review with 49 references provided. NASA funding for ISRU has been sporadic and minimal, probably because no mission was being implemented that used ISRU. The state of the technology remains underdeveloped. For example, CO2 in the M...ars atmosphere supplies carbon and oxygen. However, we still do not have a viable system to acquire CO2 and compress it with acceptable power requirements and adequate lifetime.NASA technology for recycling wastewater was developed for the International Space Station. It requires frequent attention with replenishment and replacement of subsystems. This system appears to be inadequate for Mars missions and there is no evidence that NASA has a viable plan to fix that.Read more

An experimental investigation was carried out to study the effect of circular cross-holes on the failure behavior of unidirectional glass fiber reinforced with unsaturated polyester resin composites, varying cross-ply laminates that were subjected to axial tensile load. This paper deals with the effects of the circular notch and the number of plies on nominal tensile and net tensile strengths. Tensile strengths were investigated for composites with cross-ply([0/90]. [90°/0°/90°] and [0°/90°/0°].90°), orientation and ...varying the laminate layers with a central hole, and effects of volume fraction and number of ply on mechanical properties for un-notched (smooth) and notched specimens were also studied. The results showed that increasing the number of plies has a marginal effect on tensile strength values. The fraction of volume has significant effects and for increasing the number of plies about 9% decreases in nominal tensile strength and about 11% decrease in the net tensile strength was observed. The same results were obtained with finite element analysis.Read more

The experimental team has developed a technology of step-by-step low-temperature diffusion doping of gadolinium and scandium into silicon that allows the clusters of impurity atoms to be uniformly distributed throughout the entire bulk of the silicon material. It was shown that, unlike the samples obtained under the high-temperature diffusion doping technology, in the samples obtained under the novel technology the team had detected any surface erosion or formation of alloys and silicide in the near-surface region. The authors have revealed inc...reased thermostability and radiation resistance of silicon samples dotted with clusters of impurity atoms of gadolinium and scandium.The authors have conducted comprehensive studies by using the techniques of tagged atoms, autoradiography, measurement of conductivity and Hall effect, isothermal relaxation of capacitance and current of diffusion, solubility, and electrophysical properties of scandium in silicon under various doping conditions and for a wide temperature range (1100 ÷ 1250 0С). Diffusion parameters, solubility and acceptor nature of scandium in silicon as well as thermal stability of silicon doped with gadolinium and scandium impurity atoms have been established.Read more

In materials science, the integrity and completeness of datasets are critical for robust predictive modeling. Unfortunately, material datasets frequently contain missing values due to factors such as measurement errors, data non-availability, or experimental limitations, which can significantly undermine the accuracy of property predictions. To tackle this challenge, we introduce an optimized K-Nearest Neighbors (KNN) imputation method, augmented with Deep Neural Network (DNN) modeling, to enhance the accuracy of predicting material properties.... Our study compares the performance of our Enhanced KNN method against traditional imputation techniques—mean imputation and Multiple Imputation by Chained Equations (MICE). The results indicate that our Enhanced KNN method achieves a superior R² score of 0.973, which represents a significant improvement of 0.227 over Mean imputation, 0.141 over MICE, and 0.044 over KNN imputation. This enhancement not only boosts the data integrity but also preserves the statistical characteristics essential for reliable predictions in materials science.Read more

Many UK inhabitants now adopt careers working in both university and manufacturing sectors. In a typical engineering career, a young student may now start at university, spend time in factories, often in research and development, and then return to university in later life to teach evolving innovation principles of industry. It is clear to such people that universities are expanding rapidly whereas manufacturing has declined and reached an equilibrium of output but with lower job numbers due to robotics. Manufacturing in 2022 was approximately ...8% of British GDP whereas the university sector was only about 2%. This paper analyses the growing connections and co-locations with numerous universities/manufacturing companies, predicting that convergence in both jobs and GDP contribution will occur around 2050.Read more

Fractals are complex structures that repeat themselves at several scales. Nature exhibits these in many forms like snowflakes, mountains, coastlines, the human brain/lungs/ nervous system, and many more. It appears that these are nature’s organic way of growth. Thus, there is an underlying science that works to grow or create these self-similar patterns. In this work, tin oxide-based fractals have been grown under laboratory conditions and applied to a gas-sensing field. The facile growth methodology successfully grows fractals on a large... scale. The tin oxide fractals have unique basic building units that connect and grow in different directions. These tin oxide fractals have successfully sensed ethanol vapors in the range of 20 ppm to 100 ppm. The best sensing response has also detected ethanol vapors as low as 10 ppm at room temperature with response and recovery times of 18 ± 3 s and 22 ± 5 s, respectively. The best sensing response recorded for such sensors was under 12 s. The characteristic fractal growth is attributed as the defining factor that enhances ethanol sensing at room temperature.Read more